Medical imaging data from modalities such as CT, X-ray, MR, SPECT, PET, US, etc. have various sources of uncertainty, which affect the reliability and accuracy of the diagnostic results. Besides image noise and imaging system accuracy limitations, the generation of different types of imaging biomarker maps or functional imaging maps may include significant uncertainties related to the calculation models and assumptions. For the purpose of medical diagnostics, it is common in clinical practice to visualize mainly the average or the most probable values (e.g. the conventional HU image in CT).
The literature has indicated that showing uncertainty, together with the image data, has been achieved through color maps, semi-transparency, and artificial overlay of special structures and textures. These approaches have included methods for sophisticated visualization techniques, which include both the clinical imaging information and the data uncertainty information. Unfortunately, these techniques produce complicated multi-parameter images that may not assist practical clinical diagnostics and that can be difficult to refine to make clinical decisions based on such visualization.